Kumaraswamy generalized Kappa distribution with application to stream flow data

Nawaz, Tahir; Hussain, Sajid; Ahmad, Tanvir; Naz, Farah; Abid, Muhammad

doi:10.1016/j.jksus.2018.04.005

Cited by 6 publications

(3 citation statements)

References 19 publications

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“…Generalized inverted K-G Physics data [72] Topp-Leone generalized inverted K model Physics data [73] K log-logistic Weibull model Failure times data [74] Exponentiated inverse K model Economic data [75] Beta K Burr Type X model Physics and medical data [76] Marshall-Olkin extended inverted K model Physics, failure and medical data [77] K generalized Kappa model Geological data [78] Cubic rank transmuted K model Food and industrial data [79] K Marshall-Olkin log-logistic model Physics data [67] Odd generalized exponential K model Geological and environmental data [80] K exponentiated U-quadratic model Medical data [81] K odd Burr-G Physics and engineering data [82] Exponentiated generalized K model Environmental, agriculture and engineering data [83] Size-biased K model Engineering data [84] K generalized power Weibull model Engineering data [85] Exponentiated K-Dagum model Income and lifetime data [61]…”

Section: Model Modeling Authorsmentioning

confidence: 99%

A New Extension of the Kumaraswamy Exponential Model with Modeling of Food Chain Data

Eldessouky

Hassan

Elgarhy

et al. 2023

Axioms

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Statistical models are useful in explaining and forecasting real-world occurrences. Various extended distributions have been widely employed for modeling data in a variety of fields throughout the last few decades. In this article we introduce a new extension of the Kumaraswamy exponential (KE) model called the Kavya–Manoharan KE (KMKE) distribution. Some statistical and computational features of the KMKE distribution including the quantile (QUA) function, moments (MOms), incomplete MOms (INMOms), conditional MOms (COMOms) and MOm generating functions are computed. Classical maximum likelihood and Bayesian estimation approaches are employed to estimate the parameters of the KMKE model. The simulation experiment examines the accuracy of the model parameters by employing Bayesian and maximum likelihood estimation methods. We utilize two real datasets related to food chain data in this work to demonstrate the importance and flexibility of the proposed model. The new KMKE proposed distribution is very flexible, more so than numerous well-known distributions.

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Section: Model Modeling Authorsmentioning

confidence: 99%

A New Extension of the Kumaraswamy Exponential Model with Modeling of Food Chain Data

Eldessouky

Hassan

Elgarhy

et al. 2023

Axioms

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“…Generalized distribution models have become evident in literature because of their capability to make new characterizations of the data set and where the models are shown to be suitable and efficient for that purpose. Some recently developed models are as follows: extended generalized Lindley distribution (Kantar et al, 2018), generalized log-Moyal distribution (Bhati and Ravi, 2018), s generalized normal distribution (Cordeiro et al, 2019), Marshall-Olkin Kappa distribution (Javed et al, 2019), Marshall-Olkin length biased exponential distribution (ul Haq et al, 2019), odd log-logistic Burr X distribution (Usman et al, 2019), power Burr type X distribution (Usman and Ilyas, 2020), Kumaraswamy generalized Kappa distribution (Nawaz et al, 2020), Marshall-Olkin extended inverted Kumaraswamy distribution (Usman and ul Haq, 2020), Marshall-Olkin power Lomax distribution (ul Haqet al, 2020) and Marshall-Olkin inverted Nadarajah-Haghighi distribution (Raffiq et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

A New Extension of the Inverse Paralogistic Distribution using Gamma Generator with Application

Marasigan

2023

MJST

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This study proposed a three-parameter model called the gamma inverse paralogistic (GiPL) distribution model. The probability density and cumulative distribution functions were presented together with the quantile function. Properties such as measures of reliability, the kth raw moment and moment-generating function, partial moments, order statistics, log-likelihood functions for maximum likelihood estimations, Renyi entropy and the ordering of random variables were provided. To test the performance of the parameters, a simulation study was conducted. The simulation result was assessed using the mean, bias and root mean square errors. Finally, the data set on the number of COVID-19-infected individuals per age was used to apply the model and compared with various recently developed distribution models. Results showed the superiority of the GiPL distribution model over these models.

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“…They explored twenty-seven models and explored their application in reliability engineering data. Nawaz et al (2018) generalized Kappa distribution via Kumaraswamy G class with the intention that it would be a better alternative to the generalized Kappa distribution and exploring their application in the hydrology data. Silva et al (2019) proposed the exponentiated Kum-G class and explored their application in the reliability engineering data.…”

Section: Introductionmentioning

confidence: 99%

Statistical Properties of a New Bathtub Shaped Failure Rate Model With Applications in Survival and Failure Rate Data

Arshad¹,

Iqbal²,

Mutairi³

2021

IJSP

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In this study, we proposed a flexible lifetime model identified as the modified exponentiated Kumaraswamy (MEK) distribution. Some distributional and reliability properties were derived and discussed, including explicit expressions for the moments, quantile function, and order statistics. We discussed all the possible shapes of the density and the failure rate functions. We utilized the method of maximum likelihood to estimate the unknown parameters of the MEK distribution and executed a simulation study to assess the asymptotic behavior of the MLEs. Four suitable lifetime data sets we engaged and modeled, to disclose the usefulness and the dominance of the MEK distribution over its participant models.

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Kumaraswamy generalized Kappa distribution with application to stream flow data

Cited by 6 publications

References 19 publications

A New Extension of the Kumaraswamy Exponential Model with Modeling of Food Chain Data

A New Extension of the Kumaraswamy Exponential Model with Modeling of Food Chain Data

A New Extension of the Inverse Paralogistic Distribution using Gamma Generator with Application

Statistical Properties of a New Bathtub Shaped Failure Rate Model With Applications in Survival and Failure Rate Data

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